The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A Fully Progressive Approach to Single-Image Super-Resolution
Abstract
Recent deep learning approaches to single image superresolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8×) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [35]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 2×
“In this framework, we can naturally utilize a form of curriculum learning, which is known to improve training [4] by organizing the learning process from easy (small upsampling factors) to hard (large upsampling factors).”
BackgroundMethodSuper-resolution from a single image
Glasner, D., Bagon, S., & Irani, M. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459271 · Cited by 1,889
Cited by
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
The 2018 PIRM Challenge on Perceptual Image Super-Resolution
Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., & Zelnik-Manor, L. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11021-5_21 · Cited by 593
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results
Timofte, R., Gu, S., Wu, J., Van Gool, L., Zhang, L., Yang, M.-H., Haris, M., Shakhnarovich, G., Ukita, N., Hu, S., et al. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00130 · Cited by 341
Self-citationPatch-Based Progressive 3D Point Set Upsampling
Yifan, W., Wu, S., Huang, H., Cohen-Or, D., & Sorkine-Hornung, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00611 · Cited by 317
Self-citationChannel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution
Hu, Y., Li, J., Huang, Y., & Gao, X. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2915238 · Cited by 316
Metadata
- Authors
- Wang, Y., Perazzi, F., McWilliams, B., Sorkine-Hornung, A., Sorkine-Hornung, O., & Schroers, C.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 977-97709
- OpenAlex
- W2964042923
- Cited by
- 262
- References
- 8
- FWCI
- 10.46
- Type
- conference paper
- Language
- en
- Open access
- closed
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